Crowd Learning: Improving Online Decision Making Using Crowdsourced Data
Authors: Yang Liu, Mingyan Liu
IJCAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We also evaluate the performance of our algorithms using simulated data as well as the real-world movie ratings dataset Movie Lens. |
| Researcher Affiliation | Academia | Harvard University, Cambridge MA, USA $ University of Michigan, Ann Arbor MI, USA yangl@seas.harvard.edu, mingyan@umich.edu |
| Pseudocode | No | The paper describes algorithms but does not contain a clearly labeled 'Pseudocode' or 'Algorithm' block. |
| Open Source Code | No | No explicit statement about providing open-source code or a link to a code repository was found in the paper. |
| Open Datasets | Yes | An Empirical Study Using Movie Lens We now apply the idea of crowd-learning to the Movie Lens data [KONECT, 2014] collected via a movie recommendation system. We will use Movie Lens-1M dataset. |
| Dataset Splits | No | The paper discusses evaluating performance but does not specify explicit training, validation, or test dataset splits, percentages, or absolute counts for reproducibility of data partitioning. |
| Hardware Specification | No | The paper does not provide specific details on the hardware (e.g., GPU/CPU models, memory) used for running experiments. |
| Software Dependencies | No | The paper does not provide specific software dependency details with version numbers (e.g., library or solver names with version numbers). |
| Experiment Setup | Yes | In our simulation we have ten users with five options; each user targets the top three options at each time, i.e., M = 10, K = 3, N = 5. Furthermore, for each option the reward is given by a truncated exponentially distributed random variable (bounded). The distortion factor between each pair of users for each option is generated according to a Gaussian random variable with mean 1 and variance 1. We use crowd regret to denote the sum of regrets from all users. The regret results are averaged over 50 sample realizations. |